Freely Available Conformer Generation Methods: How Good Are They?

Ebejer, Jean-Paul; Morris, Garrett M.; Deane, Charlotte M.

doi:10.1021/ci2004658

Cited by 207 publications

(227 citation statements)

References 54 publications

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“…Following the recent extensive comparison between different conformer generation tools [41], we selected three methods having the option of generating a fixed user-specified number of conformers: FROG2 [64], OPENBABEL [65] and RDKIT [66]. More precisely,…”

Section: Remd Calculations and Conformer Generation Methods Adoptedmentioning

confidence: 99%

“…Following the recent extensive comparison between different conformer generation tools [41], we selected FROG2 [64], OPENBABEL [65] and RDKIT [66]. Similar to Figure 2, Figure 3 displays the pair-wise RMSD matrices and the corresponding histogram distributions for 30 AMP configurations generated with the three methods.…”

Section: Comparison Among Different Conformational Ensemblesmentioning

confidence: 99%

“…In the following, we will simply refer to the molecule using the corresponding Protein Data Bank chemical component identifier AMP. Thus, considering the AMP neutral zwitterionic form, the most populated one at physiological pH 7.4, we first compared our conformational landscape extracted from plain MD simulations [27] with those obtained by means of: (i) Replica Exchange MD (REMD) [39]; and (ii) some freely-available conformer generation tools widely used in computer-aided drug-design [41]. We additionally generated the General AMBER Force Field (GAFF) parameters [42] for each major tautomer in the pH range 2-14 and performed µs-long MD simulations for the species bearing net charges −2, −1 and +1.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Conformational Ensembles of Small Molecules from Molecular Dynamics Simulations: Ampicillin as a Test Case

et al. 2016

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Abstract:The accurate and exhaustive description of the conformational ensemble sampled by small molecules in solution, possibly at different physiological conditions, is of primary interest in many fields of medicinal chemistry and computational biology. Recently, we have built an on-line database of compounds with antimicrobial properties, where we provide all-atom force-field parameters and a set of molecular properties, including representative structures extracted from cluster analysis over µs-long molecular dynamics (MD) trajectories. In the present work, we used a medium-sized antibiotic from our sample, namely ampicillin, to assess the quality of the conformational ensemble. To this aim, we compared the conformational landscape extracted from previous unbiased MD simulations to those obtained by means of Replica Exchange MD (REMD) and those originating from three freely-available conformer generation tools widely adopted in computer-aided drug-design. In addition, for different charge/protonation states of ampicillin, we made available force-field parameters and static/dynamic properties derived from both Density Functional Theory and MD calculations. For the specific system investigated here, we found that: (i) the conformational statistics extracted from plain MD simulations is consistent with that obtained from REMD simulations; (ii) overall, our MD-based approach performs slightly better than any of the conformer generator tools if one takes into account both the diversity of the generated conformational set and the ability to reproduce experimentally-determined structures.

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Section: Remd Calculations and Conformer Generation Methods Adoptedmentioning

confidence: 99%

Section: Comparison Among Different Conformational Ensemblesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extracting Conformational Ensembles of Small Molecules from Molecular Dynamics Simulations: Ampicillin as a Test Case

et al. 2016

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“…To maximize reproducibility, we generated conformers following a previously published protocol 35 using RDKit 90 . For each molecule, the number of rotatable bonds determined the target number of conformers, N, such that: N=50 for molecules with less than 8 rotatable bonds, N=200 for molecules with 8 to 12 rotatable bonds, and N=300 for molecules with over 12 rotatable bonds.…”

Section: Experimental Section Generating Conformer Librariesmentioning

confidence: 99%

A simple representation of three-dimensional molecular structure

Axen

Huang

Cáceres

et al. 2017

Preprint

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Statistical and machine learning approaches predict drug-to-target relationships from 2D smallmolecule topology patterns. One might expect 3D information to improve these calculations.Here we apply the logic of the Extended Connectivity FingerPrint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the Extended ThreeDimensional FingerPrint (E3FP). By integrating E3FP with the Similarity Ensemble Approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20, and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP.Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442 -0.637 kcal/mol/heavy atom.

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“…[14]; an overview of freely available conformer generators is given in Ref. [15]. Conformer generation, it seems, is a solved problem.…”

Section: Introductionmentioning

confidence: 99%

CONFECT: Conformations from an Expert Collection of Torsion Patterns

et al. 2013

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The generation of sets of low-energy conformations for a given molecule is a central task in drug design. Herein we present a new conformation generator called CONFECT that builds on our previously published library of torsion patterns. Conformations are generated as well as ranked by means of normalized frequency distributions derived from the Cambridge Structural Database (CSD). Following an incremental construction approach, conformations are selected from a systematic enumeration within energetic boundaries. The new tool is benchmarked in several different ways, indicating that it allows the efficient generation of high-quality conformation ensembles. These ensembles are smaller than those produced by state-of-the-art tools, yet they effectively cover conformational space.

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Freely Available Conformer Generation Methods: How Good Are They?

Cited by 207 publications

References 54 publications

Extracting Conformational Ensembles of Small Molecules from Molecular Dynamics Simulations: Ampicillin as a Test Case

Extracting Conformational Ensembles of Small Molecules from Molecular Dynamics Simulations: Ampicillin as a Test Case

A simple representation of three-dimensional molecular structure

CONFECT: Conformations from an Expert Collection of Torsion Patterns

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